# 447-6236-00L  Statistics for Survival Data

 Semester Spring Semester 2020 Lecturers A. Hauser Periodicity two-yearly recurring course Language of instruction English

### Courses

NumberTitleHoursLecturers
447-6236-00 VStatistics for Survival Data
Block course
10s hrs
 Mon 08:15-10:00 HG G 19.1 » 13:15-15:00 HG G 19.1 »
A. Hauser
447-6236-00 UStatistics for Survival Data
Block course.
7.5s hrs
 Mon 10:15-12:00 HG G 19.1 » 15:15-17:00 HG G 19.1 »
A. Hauser

### Catalogue data

 Abstract The primary purpose of a survival analysis is to model and analyze time-to-event data; that is, data that have as a principal endpoint the length of time for an event to occur. This block course introduces the field of survival analysis without getting too embroiled in the theoretical technicalities. Objective Presented here are some frequently used parametric models and methods, including accelerated failure time models; and the newer nonparametric procedures which include the Kaplan-Meier estimate of survival and the Cox proportional hazards regression model. The statistical tools treated are applicable to data from medical clinical trials, public health, epidemiology, engineering, economics, psychology, and demography as well. Content The primary purpose of a survival analysis is to model and analyze time-to-event data; that is, data that have as a principal endpoint the length of time for an event to occur. Such events are generally referred to as "failures." Some examples are time until an electrical component fails, time to first recurrence of a tumor (i.e., length of remission) after initial treatment, time to death, time to the learning of a skill, and promotion times for employees.In these examples we can see that it is possible that a "failure" time will not be observed either by deliberate design or due to random censoring. This occurs, for example, if a patient is still alive at the end of a clinical trial period or has moved away. The necessity of obtaining methods of analysis that accommodate censoring is the primary reason for developing specialized models and procedures for failure time data. Survival analysis is the modern name given to the collection of statistical procedures which accommodate time-to-event censored data. Prior to these new procedures, incomplete data were treated as missing data and omitted from the analysis. This resulted in the loss of the partial information obtained and in introducing serious systematic error (bias) in estimated quantities. This, of course, lowers the efficacy of the study. The procedures discussed here avoid bias and are more powerful as they utilize the partial information available on a subject or item.This block course introduces the field of survival analysis without getting too embroiled in the theoretical technicalities. Models for failure times describe either the survivor function or hazard rate and their dependence on explanatory variables. Presented here are some frequently used parametric models and methods, including accelerated failure time models; and the newer nonparametric procedures which include the Kaplan-Meier estimate of survival and the Cox proportional hazards regression model. The statistical tools treated are applicable to data from medical clinical trials, public health, epidemiology, engineering, economics, psychology, and demography as well.

### Performance assessment

 Performance assessment information (valid until the course unit is held again) Performance assessment as a semester course ECTS credits 2 credits Examiners A. Hauser Type ungraded semester performance Language of examination English Repetition Repetition possible without re-enrolling for the course unit.

### Learning materials

 No public learning materials available. Only public learning materials are listed.

### Groups

 No information on groups available.

### Restrictions

 General : Special students and auditors need a special permission from the lecturers Priority Registration for the course unit is only possible for the primary target group Primary target group Statistics MSc (436000) CAS ETH in Applied Statistics (446000) DAS ETH in Applied Statistics (447000)

### Offered in

ProgrammeSectionType
CAS in Applied StatisticsFurther CoursesW
DAS in Applied StatisticsElectivesW
Statistics MasterStatistical and Mathematical CoursesW